Application of pattern recognition techniques to the identification of aerospace acoustic sources

A pattern recognition system was developed that successfully recognizes simulated spectra of five different types of transportation noise sources. The system generates hyperplanes during a training stage to separate the classes and correctly classify unknown patterns in classification mode. A feature selector in the system reduces a large number of features to a smaller optimal set, maximizing performance and minimizing computation

Pengenalan Karakter Plat Kendaraan Bermotor Berbasis Citra dengan Menggunakan Metode Canny dan Algoritma Backpropagation

Transportation is important needs in daily life. However, there are so many problems in transportation system in our country. The one is public transportation. To overcome it, the government implement KIR. But this KIR has several weakness. One of them is manual data. As cosequnces, human error in listing can came out as the process goes on. Pattern recognition can be used to implement automatic number plate identification in this system. One of the method is canny filter. Canny filter is uses to obtain a good image in the character image acquisition. Characters based with 12X7 pixels are be converted into binary as input for Multi Layer Perceptron with 3 layers node number of each node 84, 50, 36. Artificial neural network is trained with back propagation algorithm with a learning rate parameter 0.3 and momentum 0.9. The training process will be terminated when the iteration reaches a maximum value of 10,000 or MSE (Mean Square Error) 0.0001. Recognition rate for numeral character is 100%, however recognition rate for letter character is little bit worser, 86,87%. So overall performance is 94,29% for the whole characters

Ship design optimization in the multimodal logistics framework

The significance of multimodal transportation systems has worldwide increased significantly over the past two decades. European Union, United States and Japan have increased their efforts in studying the dynamics of multimodal transportation networks. In the paper about recent research work on the optimization problem of the design of ferries and Ro-Ro cargo ships using pattern recognition techniques (Artificial Neural Networks), Multi-objective Genetic Algorithms (MOGA) and the know-how created through the EU FP6 Research Project LOGBASED. A case study on the transportation of agricultural goods from the Greek Island of Crete to the markets of Munich is presented. The results of this study prove useful for the assessment of viable solutions serving in a balanced way for the interests of shipyards, ship operators, cargo owners, banking and financial institutions, investors and government administrations in the framework of optimized transportation scenarios

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949